├── README.md
└── MFAILC.m


/README.md:
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1 | # MFAILC
2 | model free adaptive  iterative learning control
3 | 无模型自适应迭代学习控制的matlab仿真代码
4 | 


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/MFAILC.m:
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 1 | % 期望轨迹
 2 | for k = 1:1:500
 3 | 	if k < 250
 4 | 		yd(k+1) = 0.5*(-1).^(round(k/100));
 5 | 	else 
 6 | 		yd(k+1) = 0.5*sin((k*pi)/100) + 0.3*cos((k*pi)/50);
 7 |     end
 8 | end
 9 | 
10 | % 参数设置
11 | epsilon = 0.01; % 拟伪偏导重置阈值
12 | eta = 1; % 拟伪偏导更新增益
13 | rho = 0.2; % 控制律增益
14 | lamda = 1; % 控制律步长
15 | mu = 2; % 拟伪偏导更新步长
16 | 
17 | % 控制过程
18 | i_n = 60;  %迭代次数
19 | y(1:i_n,1:500) = 0;
20 | for i = 1:1:i_n
21 | 	for k = 1:1:500
22 | 		if k == 1
23 | 			phi(i,k) = 0.4; % 初始拟伪偏导
24 | 		elseif k == 2
25 | 			phi(i,k) = phi(i,k-1) + (eta*(ub(i,k-1) - 0)/(mu + norm(ub(i,k-1) - 0)^2))*(y(i,k) - 0 - phi(i,k-1)*(ub(i,k-1) - 0));
26 | 		else 
27 | 			phi(i,k) = phi(i,k-1) + (eta*(ub(i,k-1) - ub(i,k-2))/(mu + norm(ub(i,k-1) - ub(i,k-2))^2))*(y(i,k) - y(i,k-1) - phi(i,k-1)*(ub(i,k-1) - ub(i,k-2)));
28 | 		end
29 | 		if i == 1
30 | 			uf(i,k) = 0; % 初始控制信号
31 | 		else 
32 | 			uf(i,k) = uf(i-1,k) + 0.4*e(i-1,k+1);
33 | 		end
34 | 		if k == 1
35 | 			ub(i,k) = 0; % 初始控制信号
36 | 		else 
37 | 			ub(i,k) = ub(i,k-1) + (rho*phi(i,k)/(lamda + norm(phi(i,k))^2))*(yd(k+1) - y(i,k));
38 | 		end
39 | 		if k>2 && (phi(i,k) <= epsilon || (abs(ub(i,k-1) - ub(i,k-2)) <= epsilon))
40 | 			phi(i,k) = phi(i,1);
41 | 		end
42 | 		u(i,k) = uf(i,k) + ub(i,k);
43 | 		% 系统函数
44 | 		if k < 250 
45 | 			y(i,k+1) = y(i,k)*u(i,k)/(1 + norm(y(i,k))^2) + (u(i,k) + (1+*round(k/500))*sin(y(i,k)))^3;
46 | 		else
47 | 			y(i,k+1) = y(i,k)*u(i,k)^3/(1 + norm(y(i,k))^2) + u(i,k)^3;
48 | 		end
49 | 		e(i,k+1) = yd(k+1) - y(i,k+1);
50 | 	end
51 | end
52 | %误差
53 | for i =1:1:i_n
54 | e_min(i) = max(abs(e(i,:)));
55 | end
56 | 
57 | figure(1) 
58 | plot(yd,'r'); hold on;
59 | plot(y(i_n,:),'b'); title('µü´ú10´Î');
60 | figure(2)
61 | plot(e_min);title('error of time k');
62 | 


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